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This repository IS A COLLECTION OF OVER 30 DATA SCIENCE AND AI/ML CAPSTONE PROJECTS, marking my hands-on practice and assessment journey through courses on Udemy and Coursera.

Karthi-DStech/Capstone-projects-of-Data-Science-and-AI-ML

Folders and files, repository files navigation, capstone projects in data science and ai/ml.

This repository is a compilation of over 30 capstone projects , each a stepping stone in my journey to becoming proficient in Data Science and AI/ML . The projects range from fundamental machine learning algorithms to advanced neural networks and generative models, showcasing a hands-on approach to learning and applying AI/ML concepts.

These projects were developed as a part of my learning and assessments while pursuing courses on Udemy and Coursera, providing hands-on experience with a wide range of data science concepts and techniques.

Projects Summary

The repository includes diverse projects, such as:

ANN for MNIST: A project exploring artificial neural networks using the MNIST dataset.

Adaptive Boosting: Implementing AdaBoost algorithm on various datasets.

CNN for CIFAR-10: Building a convolutional neural network to classify images from the CIFAR-10 dataset.

CNN for Custom Dataset & Pretrained Model: Utilizing CNN with custom images and pretrained models like AlexNet.

CNN for MNIST: Creating a CNN to recognize handwritten digits from the MNIST dataset.

DBSCAN Project: A project on density-based spatial clustering of applications with noise (DBSCAN).

DC-GANs for MNIST: Developing deep convolutional GANs to generate digit images from the MNIST dataset.

Decision Trees Notebook: An exploration of decision tree algorithms for classification problems.

GAN using MNIST: Designing generative adversarial networks for MNIST digit generation.

Gradient Boosting Project: Applying gradient boosting techniques for predictive modeling.

Hierarchical Clustering: Delving into hierarchical clustering methods and their applications.

K-means Clustering Project: Using K-means clustering for data segmentation and pattern recognition.

K-nearest Neighbor Project: Investigating the K-nearest neighbor algorithm for both regression and classification.

Linear Regression Projects: Covering linear regression and its variants like polynomial, ridge, lasso, and elastic net regression.

Logistic Regression: Implementing logistic regression for binary classification tasks.

NLP Basic Project: A project focused on natural language processing for text classification.

PCA: Principal component analysis for dimensionality reduction in datasets.

Random Forest Projects: Utilizing random forest algorithm for both classification and regression tasks.

Supervised Learning Projects: Comprehensive projects on various supervised learning techniques.

Support Vector Machine Project: Applying support vector machines to classification problems.

Visualization Project: Projects emphasizing data visualization techniques and their importance in data science.

W-GAN GP and W-GAN using MNIST: Advanced projects implementing Wasserstein GANs with gradient penalty.

Purpose The intent behind this repository is to document the practical applications of the theoretical knowledge I've gained throughout my data science and AI/ML studies. It serves as a reflection of my ability to tackle real-world problems with advanced analytical and computational techniques. Primarily, these projects have been instrumental in building a solid foundation for undertaking larger, independent projects and motivated me to engage in data science competitions.